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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Speaker Normalization Via Springy Discriminant Analysis and Pitch Estimation
verfasst von : Dénes Paczolay, András Bánhalmi, András Kocsor
Erschienen in: Text, Speech and Dialogue
Verlag: Springer Berlin Heidelberg
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Speaker normalization techniques are widely used to improve the accuracy of speaker independent speech recognition. One of the most popular group of such methods is Vocal Tract Length Normalization (VTLN). These methods try to reduce the inter-speaker variability by transforming the input feature vectors into a more compact domain, to achieve better separations between the phonetic classes. Among others, two algorithms are commonly applied: the Maximum Likelihood criterion-based, and the Linear Discriminant criterion-based normalization algorithms. Here we propose the use of the Springy Discriminant criterion for the normalization task. In addition we propose a method for the VTLN parameter determination that is based on pitch estimation. In the experiments this proves to be an efficient and swift way to initialize the normalization parameters for training, and to estimate them for the voice samples of new test speakers.